SumTra：一种用于少样本跨语言摘要的可微流水线
SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual Summarization
AI总结：
本文提出一种端到端可微的“先摘要后翻译”流水线SumTra，通过复用单语资源实现卓越的跨语言摘要零样本性能，并在仅用10%微调样本时于多语言上超越基线。
摘要：
跨语言摘要（XLS）生成的摘要语言与输入文档语言不同（例如英语到西班牙语），使目标语言使用者能简要了解其内容。目前，该任务的主流方法是采用性能良好的预训练多语言语言模型（LM），并在目标语言对上针对XLS进行微调。然而，微调样本的稀缺在某些情况下使该方法面临挑战。因此，本文提出重新审视“先摘要后翻译”的流水线，即按顺序执行摘要和翻译任务。该方法能复用大量公开的单语摘要和翻译资源，从而获得极具竞争力的零样本性能。此外，所提流水线端到端完全可微，使其能够在条件允许时利用少样本微调。在两个当代广泛采用的XLS数据集（CrossSum和WikiLingua）上的实验表明，所提方法具有卓越的零样本性能；同时，与同等的多语言LM基线相比，它展现出强劲的少样本性能，在仅使用10%微调样本的情况下便能在多种语言中超越该基线。
英文摘要：
Cross-lingual summarization (XLS) generates summaries in a language different from that of the input documents (e.g., English to Spanish), allowing speakers of the target language to gain a concise view of their content. In the present day, the predominant approach to this task is to take a performing, pretrained multilingual language model (LM) and fine-tune it for XLS on the language pairs of interest. However, the scarcity of fine-tuning samples makes this approach challenging in some cases. For this reason, in this paper we propose revisiting the summarize-and-translate pipeline, where the summarization and translation tasks are performed in a sequence. This approach allows reusing the many, publicly-available resources for monolingual summarization and translation, obtaining a very competitive zero-shot performance. In addition, the proposed pipeline is completely differentiable end-to-end, allowing it to take advantage of few-shot fine-tuning, where available. Experiments over two contemporary and widely adopted XLS datasets (CrossSum and WikiLingua) have shown the remarkable zero-shot performance of the proposed approach, and also its strong few-shot performance compared to an equivalent multilingual LM baseline, that the proposed approach has been able to outperform in many languages with only 10% of the fine-tuning samples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
